Order-Stability in Complex Biological, Social, and AI-Systems from Quantum Information Theory

This paper is our attempt, on the basis of physical theory, to bring more clarification on the question "What is life?" formulated in the well-known book of Schrodinger in 1944. According to Schrodinger, the main distinguishing feature of a biosystem's functioning is the ability to preserve its order structure or, in mathematical terms, to prevent increasing of entropy. However, Schrodinger's analysis shows that the classical theory is not able to adequately describe the order-stability in a biosystem. Schrodinger also appealed to the ambiguous notion of negative entropy. We apply quantum theory. As is well-known, behaviour of the quantum von Neumann entropy crucially differs from behaviour of classical entropy. We consider a complex biosystem S composed of many subsystems, say proteins, cells, or neural networks in the brain, that is, S = (S-i). We study the following problem: whether the compound system S can maintain "global order" in the situation of an increase of local disorder and if S can preserve the low entropy while other S-i increase their entropies (may be essentially). We show that the entropy of a system as a whole can be constant, while the entropies of its parts rising. For classical systems, this is impossible, because the entropy of S cannot be less than the entropy of its subsystem S-i. And if a subsystems's entropy increases, then a system's entropy should also increase, by at least the same amount. However, within the quantum information theory, the answer is positive. The significant role is played by the entanglement of a subsystems' states. In the absence of entanglement, the increasing of local disorder implies an increasing disorder in the compound system S (as in the classical regime). In this note, we proceed within a quantum-like approach to mathematical modeling of information processing by biosystems-respecting the quantum laws need not be based on genuine quantum physical processes in biosystems. Recently, such modeling found numerous applications in molecular biology, genetics, evolution theory, cognition, psychology and decision making. The quantum-like model of order stability can be applied not only in biology, but also in social science and artificial intelligence.